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UID:www.tcs.tifr.res.in/event/1508
DTSTAMP:20250101T034635Z
SUMMARY:Bridging Classical and Quantum Optimization: New Directions for QAO
 A and Max-Cut
DESCRIPTION:Speaker: Jai Moondra (Georgia Institute of Technology\, USA)\n\
 nAbstract: \nThe Quantum Approximate Optimization Algorithm (QAOA) is a pr
 ominent candidate for realizing quantum advantage in combinatorial optimiz
 ation problems such as Max-Cut. While its theoretical promise is exciting\
 , practical challenges such as noise\, circuit complexity\, and hardware c
 onstraints currently limit its utility on Noisy Intermediate-Scale Quantum
  (NISQ) devices. In this talk\, I will present recent advances in addressi
 ng these challenges using classical algorithmic tools.\n \nIn the first h
 alf of the talk\, I will briefly review standard QAOA for Max-Cut before i
 ntroducing custom mixers — modified circuits designed to warm-start QAOA
  by exploiting the underlying graph structure. Using warm-starts based on 
 the Goemans-Williamson SDP\, we establish approximation guarantees for the
 se mixers and demonstrate their convergence to optimal solutions. In the s
 econd half\, I will address the challenge of physically implementing QAOA 
 circuits and present the Union-of-Stars algorithm for their construction o
 n trapped-ion quantum hardware. This algorithm\, combined with classical t
 ools like graph sparsification and decomposition\, reduces overall circuit
  noise and represents the first non-trivial construction in this setting. 
 Throughout the talk\, I will highlight both theoretical and empirical resu
 lts. Based on joint work with Bryan Gard\, Swati Gupta\, Creston D. Herold
 \, Philip C. Lotshaw\, Greg Mohler\, Joel Rajakumar\, and Reuben Tate.\nSh
 ort bio: Jai Moondra is a fourth-year PhD student at the School of Compute
 r Science at Georgia Tech\, advised by Dr. Swati Gupta (MIT) and Dr. Mohit
  Singh (Georgia Tech). He finished his BTech in Computer Science and Engin
 eering from IIT Delhi in 2019. His research broadly focuses on discrete op
 timization and its applications to algorithmic fairness\, quantum computin
 g\, and machine learning.\n \n
URL:https://www.tcs.tifr.res.in/web/events/1508
DTSTART;TZID=Asia/Kolkata:20250102T160000
DTEND;TZID=Asia/Kolkata:20250102T170000
LOCATION:A-201 (STCS Seminar Room)
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